Review

Functions - let’s inspect!

clean_if_bad_names <- function(x) {
  
  if (any(str_detect(names(x), "[\\sA-Z()/-]"))) {
    clean_names(x)
  }
  
  x
  
}

# reset df
df <- penguins_raw

clean_if_bad_names(x = df)
## # A tibble: 344 × 17
##    studyName `Sample Number` Species       Region Island Stage   `Individual ID`
##    <chr>               <dbl> <chr>         <chr>  <chr>  <chr>   <chr>          
##  1 PAL0708                 1 Adelie Pengu… Anvers Torge… Adult,… N1A1           
##  2 PAL0708                 2 Adelie Pengu… Anvers Torge… Adult,… N1A2           
##  3 PAL0708                 3 Adelie Pengu… Anvers Torge… Adult,… N2A1           
##  4 PAL0708                 4 Adelie Pengu… Anvers Torge… Adult,… N2A2           
##  5 PAL0708                 5 Adelie Pengu… Anvers Torge… Adult,… N3A1           
##  6 PAL0708                 6 Adelie Pengu… Anvers Torge… Adult,… N3A2           
##  7 PAL0708                 7 Adelie Pengu… Anvers Torge… Adult,… N4A1           
##  8 PAL0708                 8 Adelie Pengu… Anvers Torge… Adult,… N4A2           
##  9 PAL0708                 9 Adelie Pengu… Anvers Torge… Adult,… N5A1           
## 10 PAL0708                10 Adelie Pengu… Anvers Torge… Adult,… N5A2           
## # … with 334 more rows, and 10 more variables: Clutch Completion <chr>,
## #   Date Egg <date>, Culmen Length (mm) <dbl>, Culmen Depth (mm) <dbl>,
## #   Flipper Length (mm) <dbl>, Body Mass (g) <dbl>, Sex <chr>,
## #   Delta 15 N (o/oo) <dbl>, Delta 13 C (o/oo) <dbl>, Comments <chr>

But we don’t really need that silly function

clean_names() from janitor looks for more bad elements in names
How do we know?
Pro tip: cmd-click on the function name below

janitor::clean_names()


Select the data.frame method from the drop down

Then cmd click the internal function make_clean_names()

Now we see the internals on janitor’s make_clean_names(), we see it cleans up apostrophes and other things our function didn’t.

What other functions do you want to inspect?

dplyr::arrange()

Water cooler chat: Chunk options in gear

In the chunk above, the options in the gear icon do not provide a way for:
{r echo=TRUE, eval=FALSE}
which means to show the code but do not run the code. Am I missing something?

Function and snippet mashup

Have you seen people post these cool images of their code:

The carbonate package makes those.
We are going to combine some carbonate functions into one function using purrr::compose()

# compose the set of carbonate functions to open your copied code at the carbonate URL
snip <- purrr::compose(
 ~ my_code$browse(),
 ~ assign(x$template, "one-dark", pos = ".GlobalEnv"),
 ~ assign("my_code", carbon$new(), pos = ".GlobalEnv"),
 ~ library(carbonate)
)

Copy some code to your clipboard, then

(will not work if your system does not have the xclip or xsel utility)

snip()

More manually if you cannot grab code from your clipboard

x <- carbon$new(
  '
  clean_if_bad_names <- function(x) {

  if (any(str_detect(names(x), "[\\sA-Z()/-]"))) {
    clean_names(x)
  }

}
  '
)

# available templates
x$get_templates()

# chose one and send to browser
x$template <- "one-dark"
x$browse()

Back to IDE shortcuts for a moment

Comment and not

Pro tip: highlight multiple lines of code, shift-ctl-c to comment all

Move line

Pro tip: alt+up or alt+down to move lines

Multi-line

Pro tip: ctl+alt+down or ctl+alt+up to have multi-line cursor
You try: add commas after starts_with() and mean

df %>% 
  summarise(
    across(
      starts_with("culmen")
      mean
      na.rm = TRUE
      )
    )

Which parentheses?

Pro tip: double click one of the parentheses to highlight what it contains

div(
  class = "outer",
  div(
    class = "container",
    div(class = "data",
        df %>% 
          filter(
            str_detect(Species, "Chin"),
            Sex == "MALE",
            Island == "Dream"
          ) %>% 
          summarise(
            across(
              starts_with("culmen"),
              mean,
              na.rm = TRUE
            )
          )
    )
  )
)